There are several functions that hikers can use to predict walking time based on elevation change or slope of the ground. The most commonly used is the Naismith function that was first published over 100 years ago. The availability of GPS devices to record tracks now make it possible to evaluate the performance of walking time functions. Four data sources were used: 98 tracks downloaded from the Wikiloc web site; 55 tracks recorded by the author; 19 tracks recorded by the blogger Iron Hiker; and 20 tracks recorded by the blogger Hiking Guy. The .gpx files were processed to generate segements of ~100m in length, with the associated segment duration and elevation change. The association between walking pace and elevation change was assessed in the Wikiloc data using linear spline regression. The performance of the linear spline function was then compared with the Naismith, Tobler and Laingmuir functions. The linear spline performed the best, but all four performed reasonably well. While the linear spline function could easily be programmed into the software of standard GPS devices, the Naismith function provides a simple-to-use rule-of-thumb for estimating walking time for a typical hike in the mountains.

Development and validation of a new walking pace function using crowd-sourced GPS data